Expression Profiling Mark Voorhies 4/3/2012 Mark Voorhies - - PowerPoint PPT Presentation

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Jul 22, 2023 36 likes •408 views

Expression Profiling Mark Voorhies 4/3/2012 Mark Voorhies Expression Profiling Its hard work at times, but you have to be realistic. If you have a large database with many variables and your goal is to get a good understanding of the

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Expression Profiling

Mark Voorhies 4/3/2012

Mark Voorhies Expression Profiling

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It’s hard work at times, but you have to be realistic. If you have a large database with many variables and your goal is to get a good understanding of the interrelationships, then, unless you get lucky, this complex structure is bound to require some hard work to understand. Bill Cleveland and Rick Becker http://stat.bell-labs.com/project/trellis/interview.html

Mark Voorhies Expression Profiling

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Expression Profiling

Why profile transcription?

Mark Voorhies Expression Profiling

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Expression Profiling

Why profile transcription? Major mode of regulation Due to feedback, “shadows” other modes of regulation Thanks to Watson-Crick base pairing, we can assay arbitrary nucleic acids in a uniform way

Mark Voorhies Expression Profiling

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Expression Profiling Workflow

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Expression Profiling Analysis

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Sample Preparation

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Transforming Ratios

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Transforming Ratios

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Transforming Ratios

M1 = M3/M2

Mark Voorhies Expression Profiling

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Transforming Ratios

log2M1 = log2M3 − log2M2

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The CDT file format

Minimal CLUSTER input Cluster3 CDT output Tab delimited (\t) UNIX newlines (\n) Missing values → empty cells

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Comparing all measurements for two genes

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5 −5 5

Comparing two expression profiles (r = 0.97)

TLC1 log2 relative expression YFG1 log2 relative expression

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Comparing all genes for two measurements

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−5 5 10 −10 −5 5 Array 1, log2 relative expression Array 2, log2 relative expression

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Expression Profiling

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Comparing all genes for two measurements

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Euclidean Distance

Array 1, log2 relative expression Array 2, log2 relative expression

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Expression Profiling

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Comparing all genes for two measurements

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−5 5 10 −10 −5 5

Uncentered Pearson

Array 1, log2 relative expression Array 2, log2 relative expression

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Expression Profiling

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Measure all pairwise distances under distance metric

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Hierarchical Clustering

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Hierarchical Clustering

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Hierarchical Clustering

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Hierarchical Clustering

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Hierarchical Clustering

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Using the Cluster3 GUI

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Load your data

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Choose distance function

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Choose linking method

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Using JavaTreeView

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Adjust pixel settings for global view

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Adjust pixel settings for global view

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Select annotation columns

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Select annotation columns

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Select URL for gene annotations

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Select URL for gene annotations

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Activate and detach annotation window

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Activate and detach annotation window

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Activate and detach annotation window

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Homework

Compare the effects of different distance metrics and clustering algorithms on the data from the Eisen paper (note that the GORDER column for the human data will make comparison easier). Practice annotating clusters in JavaTreeView. Try to find the annotated yeast clusters from the paper. Follow the links to SGD to see if the annotations for these genes have changed in the past decade. Read Bioinformatics 20:3710 Reminder: we are in HSW-532 tomorrow!

Mark Voorhies Expression Profiling